Capsule Networks. In [1]} Hinton et al. introduced capsules, extending the idea of classical neurons by allowing them to output vectors instead of scalars. These vectors can be interpreted as attributes of an object and aim to reduce information-loss between layers of convolutional neural networks (CNN) [2]}, [3]}. Capsules require specialized routing protocols, such as routing-by-agreement [4]}, where the activation probability of a capsule is dependent on the agreement of its real inputs with their expected values. Further extensions for capsules have been proposed, such as using matrices internally [5]}, using 3D input [6]} and improving equivariance [7]}.
